These are instructions on how to use the functions in the library psrc_census.R
The code requires several libraries. The config file contains global values specific to PSRC. The main code is psrc_census.R.
Currently there are two functions:
psrc_acs_table retrieves acs data for a variable and puts it in a table at a chosen geography, for a chosen year
create_tract_map makes a map of the region at the tract level for a variable
You will need to source to files library/psrc_census_config.R and library/psrc_census.R. Make sure you enter the correct relative path to point to these files. For example, this code is running from the docs directory, so it needs to point one folder above as in ..//library
library(tidyverse)
library(sf)
library(leaflet)
library(tidycensus)
library(writexl)
source('../library/psrc_census_config.R')
source('../library/psrc_census.R')The first time you run this code, you will need to set our Census API Key as an environment variable, if you haven’t done that before. After that you can just get it. This is the website to get a key: https://api.census.gov/data/key_signup.html. Once you run Sys.setenv on the Census API Key you will only need to run Sys.getenv.
#Sys.setenv(CENSUS_API_KEY = 'PUT YOUR KEY HERE')
Sys.getenv("CENSUS_API_KEY")Next you need to decide what tables you would like to download. This is the hardest part because you have find the correct table code, decide on geography, and which years.
The main function psrc_acs_table has four parameters: tbl_code, geog, yr,acs.
tbl_code is the code of the ACS table list on the Census website, in string format, such as “B02001_005”
geog is the geography, currently with the options of “county” or “tract”
yr is the last numeric year of the data, such as 2019 for 2014-2019 five year, or 2019 for one year
acs is which dataset you are using, such as “acs1” or “acs5”
You can find the list of ACS datasets available via the api here: https://www.census.gov/data/developers/data-sets.html
THe function to make a table is defined like this: psrc_table(tbl_code, geog, yr,acs)
Here are a few examples of using the function.
tract_asian<-psrc_acs_table("B02001_005", "tract", 2019,'acs5')## Getting data from the 2015-2019 5-year ACS## # A tibble: 776 x 10
## GEOID NAME variable estimate moe ACS_Year ACS_Type ACS_Geography label
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033~ Census~ B02001_~ 1202 248 2019 acs5 tract Estim~
## 2 53033~ Census~ B02001_~ 1168 232 2019 acs5 tract Estim~
## 3 53033~ Census~ B02001_~ 484 155 2019 acs5 tract Estim~
## 4 53033~ Census~ B02001_~ 970 418 2019 acs5 tract Estim~
## 5 53033~ Census~ B02001_~ 465 194 2019 acs5 tract Estim~
## 6 53033~ Census~ B02001_~ 278 143 2019 acs5 tract Estim~
## 7 53033~ Census~ B02001_~ 1094 355 2019 acs5 tract Estim~
## 8 53033~ Census~ B02001_~ 997 254 2019 acs5 tract Estim~
## 9 53033~ Census~ B02001_~ 412 195 2019 acs5 tract Estim~
## 10 53033~ Census~ B02001_~ 323 64 2019 acs5 tract Estim~
## # ... with 766 more rows, and 1 more variable: concept <chr>county_ferry<-psrc_acs_table("B08006_013", "county", 2019, 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2019 1-year ACS## # A tibble: 4 x 10
## GEOID NAME variable estimate moe ACS_Year ACS_Type ACS_Geography label
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 53033 King ~ B08006_~ 1687 854 2019 acs1 county Estimate~
## 2 53035 Kitsa~ B08006_~ 9788 2076 2019 acs1 county Estimate~
## 3 53053 Pierc~ B08006_~ 118 124 2019 acs1 county Estimate~
## 4 53061 Snoho~ B08006_~ 61 106 2019 acs1 county Estimate~
## # ... with 1 more variable: concept <chr>region_female_under_5<-psrc_acs_table("B01001_027", "region", 2019, 'acs1')## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2019 1-year ACS## `summarise()` has grouped output by 'variable', 'ACS_Year', 'ACS_Type'. You can override using the `.groups` argument.## # A tibble: 1 x 6
## # Groups: variable, ACS_Year, ACS_Type [1]
## variable total_region moe_region ACS_Year ACS_Type label
## <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 B01001_027 125373 1275. 2019 acs1 Estimate!!Total:!!Female~#if you want you can write the data to the clipboard or out to csv or excel
write.table(county_ferry, "clipboard", sep="\t", row.names=FALSE)
write.csv(county_ferry, "ferry_workers_by_county.csv")
write_xlsx(county_ferry, "ferry_workers_by_county.xlsx")create_tract_map is function in which you send in a data frame by tract, and it makes a map. The parameter is takes in is one ACS variable by tract.
create_tract_map(tract_asian)## Reading layer `dbo.tract2010_nowater' from data source `MSSQL:server=AWS-PROD-SQL\Sockeye;database=ElmerGeo;trusted_connection=yes' using driver `MSSQLSpatial'
## Simple feature collection with 773 features and 19 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 1099353 ymin: -97548.53 xmax: 1622631 ymax: 477101.5
## projected CRS: NAD83 / Washington North (ftUS)